Would it be accurate to claim that
streamlit.cache is designed more for cacheing data import activity rather than the outputs of computation? I’m asking because, based on the limited time I have spent with it, it has problems with hashing objects used inside of function calls. For example, I am using
rpy2 to call an R script inside of a function, but it cannot deal with one or more of the objects it uses:
Streamlit cannot hash an object of type <class 'rpy2.robjects.conversion.Converter'>.
Is there a way of making cacheing work with arbitrary function calls? I was hoping that the output (in this case, a numpy array) was all that was being cached, but it looks like the serialization goes deeper than that.